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Interactive Deep Learning for Congenital Heart Disease Segmentation

Key Investigators

• Danielle Pace (MIT)
• Adrian Dalca (MIT)
• Polina Golland (MIT)
• Mehdi Hedjazi Moghari (Boston Children’s Hospital)

Project Description

Objective

1. Aim: segment all cardiac chambers and great vessels from cardiac MRI, for children with congenital heart disease.
2. 20 training cases + large anatomical variability - remains a challenge for automatic segmentation.
3. Approach: Integrate some interaction from the user, e.g. scribbles or landmarks.

Approach and Plan

1. Already have framework for interactive segmentation. Currently testing using scribbles for aorta segmentation.
2. Investigate data augmentation to prevent overfitting - noise / slight intensity changes / small deformations.
3. Parameter tuning.

Progress and Next Steps

1. Implemented on-the-fly data augmentation, including (1) random affine transformations constrained by a user-specified maximum rotation, translation, scale and shear, and (2) random elastic deformation.
2. Currently running trials to measure impact and tune parameters.

Illustrations

Background and References

• HVSMR Challenge Data: (http://segchd.csail.mit.edu)